The benefits of medical AI assistance vary based on user expertise
A one-size-fits-all approach likely isn’t the best strategy when designing artificial intelligence systems that assist users in disease diagnosis.
A new study by researchers at MIT and elsewhere found that, while AI assistance generally improved the accuracy of non-experts and clinicians in diagnosing skin diseases, AI explainability methods had different impacts depending on the users’ knowledge level.
Explainable AI methods help users know when to trust a model’s predictions by describing or validating the model’s decision-making. For instance, a model might use a heat map to highlight image regions that were most important in its diagnosis or a large language model (LLM) to explain the prediction in plain language.
In this study, researchers tested non-experts and primary care providers in skin disease diagnosis, with and without the help of different explainable AI systems.
They found that non-experts’ diagnostic accuracy improved, but it was largely due to deference to the AI...
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